Despite the common finding that crime is highly concentrated in space, the specific locations, characteristics, and contexts of crime hot spots are not consistently the same or generalizable across unique units of analysis and crime types. This dissertation will simultaneously integrate different types of environmental criminology predictors, including crime generators and attractors and environmental disorder indicators, to best identify the situational predictors of hot spot street segments relative to empirical controls in Indianapolis for several different crime types. Hot spot and control units will be compared based on the presence of spatially joined crime generators and attractors and environmental disorder indicators recorded via remote systematic social observation using Google Street View. In addition to uncovering the strongest environmental predictors significantly more likely to predict hot spots, this dissertation will also determine the level of spatial overlap of hot spots across different crime types and statistically assess the consistency of the influence of environmental predictors across each of the separate crime types. The findings will provide new information regarding the locations and composition of different types of crime hot spots at highly localized spatial extents. Also, this dissertation stands to make several methodological contributions by using remote systematic social observation to measure disorder and an innovative case-control research design that empirically matches hot spot and control units within a predefined spatial parameter while holding several key covariates constant.
","abstract_html":"<p>Despite the common finding that crime is highly concentrated in space, the specific locations, characteristics, and contexts of crime hot spots are not consistently the same or generalizable across unique units of analysis and crime types. This dissertation will simultaneously integrate different types of environmental criminology predictors, including crime generators and attractors and environmental disorder indicators, to best identify the situational predictors of hot spot street segments relative to empirical controls in Indianapolis for several different crime types. Hot spot and control units will be compared based on the presence of spatially joined crime generators and attractors and environmental disorder indicators recorded via remote systematic social observation using Google Street View. In addition to uncovering the strongest environmental predictors significantly more likely to predict hot spots, this dissertation will also determine the level of spatial overlap of hot spots across different crime types and statistically assess the consistency of the influence of environmental predictors across each of the separate crime types. The findings will provide new information regarding the locations and composition of different types of crime hot spots at highly localized spatial extents. Also, this dissertation stands to make several methodological contributions by using remote systematic social observation to measure disorder and an innovative case-control research design that empirically matches hot spot and control units within a predefined spatial parameter while holding several key covariates constant.</p>","abstract_has_math":false,"creators":["Connealy, Nathan T."],"institution":"The Graduate School and University Center of The City University of New York","degree_name":"Doctor of Philosophy","degree_level":"Doctoral","degree_discipline":"Criminal Justice","degree_department":null,"school":null,"contributors":[],"advisors":["Eric L. Piza"],"committee_chairs":[],"committee_members":["Kevin T. Wolff","Brian Lawton","Joel Miller"],"year":2021,"date_issued":"2021-06-01T07:00:00Z","date_published":"2021-06-01T07:00:00Z","updated_at":"2026-07-24T02:00:26Z","subjects":["Google Street View","Environmental Criminology","Generators","Attractors","Disorder","Decay"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/gc_etds/4315","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Eric L. Piza"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Kevin T. 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This dissertation will simultaneously integrate different types of environmental criminology predictors, including crime generators and attractors and environmental disorder indicators, to best identify the situational predictors of hot spot street segments relative to empirical controls in Indianapolis for several different crime types. Hot spot and control units will be compared based on the presence of spatially joined crime generators and attractors and environmental disorder indicators recorded via remote systematic social observation using Google Street View. In addition to uncovering the strongest environmental predictors significantly more likely to predict hot spots, this dissertation will also determine the level of spatial overlap of hot spots across different crime types and statistically assess the consistency of the influence of environmental predictors across each of the separate crime types. The findings will provide new information regarding the locations and composition of different types of crime hot spots at highly localized spatial extents. Also, this dissertation stands to make several methodological contributions by using remote systematic social observation to measure disorder and an innovative case-control research design that empirically matches hot spot and control units within a predefined spatial parameter while holding several key covariates constant.
"]},{"key":"dc:title","label":"Title","values":["Exploring the Overlap, Saliency, and Consistency of Environmental Predictors in Crime Hot Spots: A Remote Systematic Social Observation and Case-Control Examination"]}]}],"canonical_facts":{"dc:contributor.advisor":["Eric L. Piza"],"dc:contributor.committeemember":["Kevin T. Wolff","Brian Lawton","Joel Miller"],"dc:creator":["Connealy, Nathan T."],"dc:date.available":["2021-04-29T07:00:00Z"],"dc:description.abstract":["Despite the common finding that crime is highly concentrated in space, the specific locations, characteristics, and contexts of crime hot spots are not consistently the same or generalizable across unique units of analysis and crime types. This dissertation will simultaneously integrate different types of environmental criminology predictors, including crime generators and attractors and environmental disorder indicators, to best identify the situational predictors of hot spot street segments relative to empirical controls in Indianapolis for several different crime types. Hot spot and control units will be compared based on the presence of spatially joined crime generators and attractors and environmental disorder indicators recorded via remote systematic social observation using Google Street View. In addition to uncovering the strongest environmental predictors significantly more likely to predict hot spots, this dissertation will also determine the level of spatial overlap of hot spots across different crime types and statistically assess the consistency of the influence of environmental predictors across each of the separate crime types. The findings will provide new information regarding the locations and composition of different types of crime hot spots at highly localized spatial extents. Also, this dissertation stands to make several methodological contributions by using remote systematic social observation to measure disorder and an innovative case-control research design that empirically matches hot spot and control units within a predefined spatial parameter while holding several key covariates constant.
"],"dc:identifier":["https://academicworks.cuny.edu/gc_etds/4315"],"dc:subject":["Google Street View","Environmental Criminology","Generators","Attractors","Disorder","Decay"],"dc:title":["Exploring the Overlap, Saliency, and Consistency of Environmental Predictors in Crime Hot Spots: A Remote Systematic Social Observation and Case-Control Examination"],"thesis:degree_discipline":["Criminal Justice"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["The Graduate School and University Center of The City University of New York"]},"updated_at":"2026-07-24T02:00:26Z"}